Semantic texture mapping system

ABSTRACT

A semantic texture map system to generate a semantic texture map based on a 3D model that comprises a plurality of vertices that include coordinate that indicate positions of the plurality of vertices, a UV map, and a semantic segmentation image that comprises a set of semantic labels.

PRIORITY CLAIM

This application is a continuation of and claims the benefit of priorityof U.S. patent application Ser. No. 16/372,215, filed on Apr. 1, 2019,which is hereby incorporated by reference herein in its entirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to texturemapping, and more particularly, to systems for generating semantictexture maps.

BACKGROUND

A “texture map” is a two-dimensional (2D) image file that can be appliedto the surface of a three-dimensional (3D) model to add color, texture,or other surface details. Texture maps are typically developed todirectly correspond with the “UV” coordinates of a 3D model and may bebased on photographs or other images. The letters “U” and “V” denote theaxes of a 2D texture (because “X,” “Y,” and “Z” are already used todenote the axes of 3D objects).

For example, when a 3D model, such as a polygon mesh, is created, a UVmap that includes a set of UV coordinates can be generated based on thepositions of the vertices of the mesh. The UV map therefore comprises aprojection of a 2D image upon a surface of the 3D model.

Although traditional computational texture analysis provides adequatemeans for producing texture maps to be used in various applications,there remains a disparity between “visual” and “semantic” features of aspace. This means they are unsuitable for applications intended for highlevels of user interaction.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure number in which that element is first introduced.

FIG. 1 is a block diagram showing an example messaging system forexchanging data (e.g., messages and associated content) over a networkin accordance with some embodiments, wherein the messaging systemincludes a semantic texture map system.

FIG. 2 is block diagram illustrating further details regarding amessaging system, according to example embodiments.

FIG. 3 is a block diagram illustrating various modules of a semantictexture map system, according to certain example embodiments.

FIG. 4 is a flowchart illustrating a method for generating a texture mapthat includes semantic labels, according to certain example embodiments.

FIG. 5 is a flowchart illustrating a method for presenting content basedon a semantic feature based on a texture map that include semanticlabels, according to certain example embodiments.

FIG. 6 is a flowchart illustrating a method for applying a semantictexture map to a presentation of an environment at a client device,according to certain example embodiments.

FIG. 7 is a flow diagram illustrating a method for generating a semantictexture map, according to certain example embodiments.

FIG. 8 is an interface diagram depicting augmented reality (AR) contentpresented based on semantic labels of a semantic texture map, accordingto certain example embodiments.

FIG. 9 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described and used to implement variousembodiments.

FIG. 10 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

As discussed above, texture mapping is a method for defining details,surface textures, or color information of a 3D model. While existingtexture mapping systems are adequate for adding visual details to 3Dmodels, they fail to provide a means for assigning properties and labelsto various surfaces of the 3D models, thereby making them less thanideal for applications where high levels of user interaction is to beexpected. Example embodiments described herein therefore relate tosystems and methods for generating a “semantic” texture map of a 3Dobject, where the semantic texture map defines a set of semanticsegmentation labels of a particular 3D object.

Example embodiments described herein relate to a semantic texture mapsystem to perform operations that include: accessing an image thatcomprises a set of image features; retrieving a semantic segmentationimage based on the set of image features of the image, the semanticsegmentation image comprising a set of semantic labels; generating a 3Dmodel, such as a. 3D mesh model, based on the set of image features ofthe image, the 3D model comprising a plurality of vertices that includecoordinate that indicate positions of the plurality of vertices;generating a UV map based on the set of image features and thecoordinates of the plurality of vertices of the 3D model, the UV mapcomprising a set of two-dimensional (2D) texture coordinates based onthe coordinates of the plurality of vertices of the 3D mesh model;projecting the semantic segmentation image onto the 3D mesh model basedon the 2D texture coordinates of the UV map; mapping the set of semanticlabels of the semantic segmentation image to the 2D texture coordinatesof the UV map; and generating a semantic texture map that comprises aset of texels, wherein the texels each include one or more semanticlabels and a set of 2D texture coordinates.

“Semantic segmentation” describes computer vision techniques to predictclass labels for each pixel of an image. A semantic segmentation imagetherefore comprises an image that includes a set of class labels thatidentify elements or objects depicted by the pixels of the image.According to certain embodiments, a semantic segmentation image can begenerated by the semantic texture map system through a semanticsegmentation neural network.

For example, the semantic texture map system may receive, as inputs froma plurality of distributed client devices, image data that includesmetadata identifying a location or object. The image data may beclassified and sorted by the semantic texture map system based on thecorresponding image metadata. The semantic texture map system maymaintain a database that comprises collections of image data sortedbased on a location or object depicted by the image data. The semanticsegmentation neural network may then be trained to generate semanticsegmentation images that comprise semantic class labels that identifysemantic features of pixels in an image, based on the collections ofimages from the database.

Semantic features may for example include: contextual features thatcorrespond with a physical object, location, or surface; analogicalfeatures that reference some other known category or class; visualfeatures that define visual or graphical properties of a surface orobject; as well as material parameters that define properties of asurface or object and which may include a “roughness value,” a.“metallic value,” a “specular value,” and a “base color value.”

Accordingly, responsive to receiving or accessing an image thatcomprises image metadata, and a set of image features, the semantictexture map system generates or retrieves a semantic segmentation imagebased on the image (the image features of the image or the imagemetadata of the image), where in the semantic segmentation imagecomprises a set of semantic feature labels assigned to the to pixels ofthe image.

The semantic texture map system generates a 3D model based on the imageaccessed by the semantic texture map system. For example, the semantictexture map system may identify a location or object depicted in theimage based on the image metadata or the image features and retrieves acollection of image depicted the location or object from a database. Thesemantic texture map system processes the collection of images through a3D reconstruction pipeline to obtain a dense 3D mesh (i.e., a 3D model)based on the collection of images that correspond with the location orobjected depicted in the image accessed, where the dense 3D meshcomprises a plurality of vertices, where each vertex among the pluralityof vertices includes corresponding coordinates. Similarly, the semantictexture map system generates a UV map based on the image and thecollection of images, wherein the UV map comprises a set of 2D texturecoordinates to project a 2D image upon the 3D mesh. For example, the 3Dmesh may be “wrapped” with a 2D image based on the 2D texturecoordinates of the UV map.

The semantic texture map system projects the semantic segmentation imagethat corresponds with the image upon the 3D model based on thecoordinates of the vertices and maps the semantic feature labels ofsemantic segmentation image to the 2D texture coordinates of the UN map.The resulting semantic texture map therefor comprises a set of texels,where each texel comprises one or more semantic feature labels, and 2Dtexture coordinates based on the UV map.

According to certain embodiments, the semantic texture map generated bythe semantic texture map system may be applied to one or more objectsdetected within a presentation of an environment displayed at a clientdevice. For example, the semantic texture map may be associated with aset of coordinates that correspond with an object (i.e., a building, astructure, a sculpture). Responsive to detecting a client device at orwithin a threshold distance of the object (i.e., based on a geo-fence),the semantic texture map system accesses and applies the semantictexture map to a depiction of the object within a presentation of anenvironment that includes the object at a display of the client device.For example, the texture map may be applied to the object as an overlay,such that the texels of the texture map occupy corresponding positionsof a surface of the object, based on the coordinates of the vertices ofthe 3D model.

By applying the texture map that includes the semantic feature labels tothe object in the presentation of the environment at the client device,AR content may be accessed and presented within the presentation of theenvironment based on the semantic feature labels. As discussed above,the semantic feature labels of the texels of the texture map may provideclassification information for a feature of an object presented in thepresentation of the environment. For example, a semantic feature labelmay include an identification of semantic information of the texels,such as which texels are “doors,” windows,” “walls,” “roofs,” or“chimney,” as well as providing information about material properties ofthe features represented by the texels, including but not limited to“hardness,” “softness,” “flexibility,” and “gloss.”

Doing so enabled the AR content to more realistically interact with theuser through the presentation of the space at the client device. As anillustrative example, by labeling which features of a space are“chimneys,” AR content may be accessed and presented which interactswith the feature realistically. For example, an AR “Santa Claus” may beaccessed and presented within a presentation of a space, responsive todetecting semantic feature labels such as “chimney,” and “roof.” In someembodiments, other attributes may be taken into account to select the ARcontent, such as user profile information, temporal information,seasonal information, and location information.

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple client devices 102, each ofwhich hosts a number of applications including a messaging clientapplication 104. Each messaging client application 104 iscommunicatively coupled to other instances of the messaging clientapplication 104 and a messaging server system 108 via a network 106(e.g., the Internet).

Accordingly, each messaging client application 104 is able tocommunicate and exchange data with another messaging client application104 and with the messaging server system 108 via the network 106. Thedata exchanged between messaging client applications 104, and between amessaging client application 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed by either a messaging client application 104 or by themessaging server system 108, it will be appreciated that the location ofcertain functionality either within the messaging client application 104or the messaging server system 108 is a design choice. For example, itmay be technically preferable to initially deploy certain technology andfunctionality within the messaging server system 108, but to latermigrate this technology and functionality to the messaging clientapplication 104 where a client device 102 has a sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Insome embodiments, this data includes, message content, client deviceinformation, geolocation information, media annotation and overlays,message content persistence conditions, social network information, andlive event information, as examples. In other embodiments, other data isused. Data exchanges within the messaging system 100 are invoked andcontrolled through functions available via GUIs of the messaging clientapplication 104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

Dealing specifically with the Application Program Interface (API) server110, this server receives and transmits message data (e.g., commands andmessage payloads) between the client device 102 and the applicationserver 112. Specifically, the Application Program Interface (API) server110 provides a set of interfaces (e.g., routines and protocols) that canbe called or queried by the messaging client application 104 in order toinvoke functionality of the application server 112. The ApplicationProgram Interface (API) server 110 exposes various functions supportedby the application server 112, including account registration, loginfunctionality, the sending of messages, via the application server 112,from a particular messaging client application 104 to another messagingclient application 104, the sending of media files (e.g., images orvideo) from a messaging client application 104 to the messaging serverapplication 114, and for possible access by another messaging clientapplication 104, the setting of a collection of media data (e.g.,story), the retrieval of a list of friends of a user of a client device102, the retrieval of such collections, the retrieval of messages andcontent, the adding and deletion of friends to a social graph, thelocation of friends within a social graph, opening and application event(e.g., relating to the messaging client application 104).

The application server 112 hosts a number of applications andsubsystems, including a messaging server application 114, an imageprocessing system 116, a social network system 122, and a semantictexture map system 124. The messaging server application 114 implementsa number of message processing technologies and functions, particularlyrelated to the aggregation and other processing of content (e.g.,textual and multimedia content) included in messages received frommultiple instances of the messaging client application 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories, galleries, or collections). These collections are then madeavailable, by the messaging server application 114, to the messagingclient application 104. Other processor and memory intensive processingof data may also be performed server-side by the messaging serverapplication 114, in view of the hardware requirements for suchprocessing.

The application server 112 also includes an image processing system 116that is dedicated to performing various image processing operations,typically with respect to images or video received within the payload ofa message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions services and makes these functions and services available tothe messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph 304 within thedatabase 120. Examples of functions and services supported by the socialnetwork system 122 include the identification of other users of themessaging system 100 with which a particular user has relationships oris “following,” and also the identification of other entities andinterests of a particular user.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is block diagram illustrating further details regarding themessaging system 100, according to example embodiments. Specifically,the messaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of some subsystems, namely an ephemeral timer system 202, acollection management system 204 and an annotation system 206.

The ephemeral tinier system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message,collection of messages, or graphical element, selectively display andenable access to messages and associated content via the messagingclient application 104. Further details regarding the operation of theephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managingcollections of media (e.g., a media collection that includes collectionsof text, image video and audio data). In some examples, a collection ofcontent (e.g., messages, including images, video, text and audio) may beorganized into an “event gallery” or an “event story.” Such a collectionmay be made available for a specified time period, such as the durationof an event to which the content relates. For example, content relatingto a music concert may be made available as a “story” for the durationof that music concert. The collection management system 204 may also beresponsible for publishing an icon that provides notification of theexistence of a particular collection to the user interface of themessaging client application 104.

The collection management system 204 furthermore includes a curationinterface 208 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface208 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation may be paid to a user for inclusion of usergenerated content into a collection. In such cases, the curationinterface 208 operates to automatically make payments to such users forthe use of their content.

The annotation system 206 provides various functions that enable a userto annotate or otherwise modify or edit media content, such as usersupport content received by the user to be forwarded or redistributed toone or more recipients. For example, the annotation system 206 providesfunctions related to the generation and publishing of media overlays formessages processed by the messaging system 100. The annotation system206 operatively supplies a media overlay to the messaging clientapplication 104 based on a geolocation of the client device 102. Inanother example, the annotation system 206 operatively supplies a mediaoverlay to the messaging client application 104 based on otherinformation, such as, social network information of the user of theclient device 102. A media overlay may include audio and visual contentand visual effects, as well as augmented reality overlays. Examples ofaudio and visual content include pictures, texts, logos, animations, andsound effects, as well as animated facial models, image filters, andaugmented reality media content. An example of a visual effect includescolor overlaying. The audio and visual content or the visual effects canbe applied to a media content item (e.g., a photo or video or livestream) at the client device 102. For example, the media overlayincluding text that can be overlaid on top of a photograph generatedtaken by the client device 102. In another example, the media overlayincludes an identification of a location overlay (e.g., Venice beach), aname of a live event, or a name of a merchant overlay (e.g., BeachCoffee House). In another example, the annotation system 206 uses thegeolocation of the client device 102 to identify a media overlay thatincludes the name of a merchant at the geolocation of the client device102. The media overlay may include other indicia associated with themerchant. The media overlays may be stored in the database 120 andaccessed through the database server 118.

In one example embodiment, the annotation system 206 provides auser-based publication platform that enables users to select ageolocation on a map, and upload content associated with the selectedgeolocation. The user may also specify circumstances under which aparticular media overlay should be offered to other users. Theannotation system 206 generates a media overlay that includes theuploaded content and associates the uploaded content with the selectedgeolocation.

In another example embodiment, the annotation system 206 provides amerchant-based publication platform that enables merchants to select aparticular media overlay associated with a geolocation. For example, theannotation system 206 associates the media overlay of a highest biddingmerchant with a corresponding geolocation for a predefined amount oftime

FIG. 3 is a block diagram illustrating components of the semantictexture map system 124 that configure the semantic texture map system124 to generate semantic texture maps by performing operations thatinclude: accessing an image that comprises a set of image features;retrieving a semantic segmentation image in response to the accessingthe image that comprises the set of image features, where the semanticsegmentation image comprises a set of semantic labels; generating a 3Dmodel based on at least the set of image features of the image inresponse to the accessing the image, wherein the 3D model comprises aplurality of vertices that include coordinates; generating a UV mapbased on the set of image features and the coordinates of the pluralityof vertices of the 3D mesh model, the UV map comprising a set of 2Dtexture coordinates based on the coordinates of the plurality ofvertices; projecting the semantic segmentation image onto the 3D modelbased on the 2D texture coordinates of the UV map; mapping the set ofsemantic labels of the semantic segmentation image to the 2D texturecoordinates; and generating a texture map that comprises a set of texelsbased on the semantic labels the UV map, and the 3D mesh model,according to certain example embodiments.

The semantic texture map system 124 is shown as including a image module302, a 3D mesh module 304, a UV map module 306, and a semantic labelingmodule 308, all configured to communicate with each other (e.g., via abus, shared memory, or a switch). Any one or more of these modules maybe implemented using one or more processors 310 (e.g., by configuringsuch one or more processors to perform functions described for thatmodule) and hence may include one or more of the processors 310.

Any one or more of the modules described may be implemented usinghardware alone (e.g., one or more of the processors 310 of a machine) ora combination of hardware and software. For example, any moduledescribed of the semantic texture map system 124 may physically includean arrangement of one or more of the processors 310 (e.g., a subset ofor among the one or more processors of the machine) configured toperform the operations described herein for that module. As anotherexample, any module of the semantic texture map system 124 may includesoftware, hardware, or both, that configure an arrangement of one ormore processors 310 (e.g., among the one or more processors of themachine) to perform the operations described herein for that module.Accordingly, different modules of the semantic texture map system 124may include and configure different arrangements of such processors 310or a single arrangement of such processors 310 at different points intime. Moreover, any two or more modules of the semantic texture mapsystem 124 may be combined into a single module, and the functionsdescribed herein for a single module may be subdivided among multiplemodules. Furthermore, according to various example embodiments, modulesdescribed herein as being implemented within a single machine, database,or device may be distributed across multiple machines, databases, ordevices.

FIG. 4 is a flowchart illustrating a method 400 for generating a texturemap that includes semantic labels, according to certain exampleembodiments. Operations of the method 400 may be performed by themodules described above with respect to FIG. 3. As shown in FIG. 4, themethod 400 includes one or more operations 402, 404, 406, 408, 410, 412,and 414.

At operation 402, the image module 302 accesses an image that comprisesa set of image features. For example, the image module 302 may receivethe image from a client device 102, wherein the image includes an imagepresented at a display of the client device 102 and depicts a real-worldenvironment. For example, the client device 102 may include one or morecameras configured to generate and stream image data to be presented atthe display of the client device 102, or at an auxiliary device (e.g.,streaming video data from the client device 102 to a separate display).

In some embodiments, the image accessed by the image module 302 mayinclude image metadata or other identifying features. For example, theimage metadata may identify a location based on geo-locationcoordinates, or the identifying features may include a barcode or otherscannable coded image.

At operation 404, the semantic labeling module 308 retrieves a semanticsegmentation image that comprises a set of semantic labels, based on theimage features of the image. For example, the semantic labeling module308 may access a repository, such as the database 120, to retrieve asemantic segmentation image that corresponds with the image, based onthe image features or metadata of the image.

In some embodiments, the semantic labeling module 308 generates thesemantic segmentation image for a location or object based on a semanticsegmentation neural network. For example, the semantic labeling module308 may access an image repository that comprises collections of imagesassociated with a location or object and use the collections of imagesto train the semantic segmentation neural network to generate a semanticsegmentation image that includes a set of semantic labels.

At operation 406, the 3D mesh module 304 generates a 3D mesh model basedon the set of image features of the image, where the 3D mesh modelcomprises a plurality of vertices that include coordinates. For example,the 3D mesh module 304 may identify an object depicted in the imagebased on the set of image features of the image metadata, and retrieve acollection of images associated with the object or location, where thecollection of images comprises a set of images that depict the object orlocation depicted by the image accessed by the image module 302. The 3Dmesh module 304 process the images through a 3D reconstruction pipelineto obtain a dense 3D mesh representation of the object or locationdepicted in the image or collection of images, and a 3D pose relative tothe object.

At operation 408, the UV map module 306 generates a UV map based on theset of image features of the image and the coordinates of the pluralityof vertices of the 3D mesh model. The UV map may therefore comprise aset of 2D texture coordinates that correspond with the coordinates ofthe vertices of the 3D mesh model. By applying a 2D image to the UV map,the 3D mesh model may be “wrapped” by the 2D image.

A 2D image may be generated based on the image accessed by the imagemodule 302 or based on the collection of images from the database 120.For example, the 2D image may include a photo of an object, or an imagegenerated based on the collection of images that depict the object fromthe database 120.

At operation 410, the semantic labeling module 308 projects the semanticsegmentation image upon the 3D mesh model based on the 2D texturecoordinates from the UV map, and at operation 412, maps the set ofsemantic labels of the semantic segmentation image to the 2D texturecoordinates.

At operation 414, the semantic labeling module 308 generates a semantictexture map based on the mapping of the semantic labels to the 2Dtexture coordinates of the UV map. The texture map comprises a set oftexels, wherein each texel includes 2D texture coordinates, and one ormore semantic labels. The semantic texture map may therefore comprise amapping of the semantic labels to the surfaces of the 3D mesh based onthe 2D texture coordinates.

In some embodiments, the semantic labeling module 308 may associate thesemantic texture map with an object category or location within adatabase, such as the databases 120. For example, the semantic texturemap may be associated with a set of geolocation coordinates thatidentify a location of interest, or object at a location of interest. Asan illustrative example, the semantic texture map may be generated basedon a building or structure at a specific location identified by a set ofgeo-location coordinates, wherein the semantic labels of the semantictexture map assign semantic feature descriptors to one or more elementsof the building or structure. For example, a texel of the semanticfeature map may indicate that a particular surface of the building orstructure is a doorway, or an exit of the building or structure.

FIG. 5 is a flowchart illustrating a method 500 for presenting contentbased on a semantic feature based on a texture map that include semanticlabels, according to certain example embodiments. Operations of themethod 500 may be performed by the modules described above with respectto FIG. 3. As shown in FIG. 5, the method 500 includes one or moreoperations 502, 504, and 506, that may be performed as a part of (e.g.,a subroutine) the method 400 depicted in FIG. 4. For example, the method500 may be performed subsequent to the operations of the method 400.

For example, subsequent to generating the semantic texture map, thesemantic texture map system 124 may associate the semantic texture mapwith a location of interest or object. Responsive to detecting a clientdevice 102 at the location of interest or receiving image data thatidentifies the object or location of interest, the semantic texture mapsystem 124 retrieves the semantic texture map and applies it to theimage data received from the client device 102. The semantic texture mapmay be overlaid upon an image presented at a display of the clientdevice 102, where a position of the semantic texture map in the image isbased on features of the image itself. For example, the semantic texturemap may be applied to a building depicted in an image presented at theclient device 102, where the semantic labels of the semantic texture mapidentify and assign properties to one or more features of the building(e.g., doorway, exit, window, roof, chimney, bouncy floor).

At operation 502, the semantic labeling module 308 assigns a materialparameter to a feature class that corresponds with a semantic label fromamong the set of semantic labels included in a semantic texture map. Forexample, an administrator of the semantic texture map system 124 mayassign a material parameter to a class identified by one or moresemantic feature labels within the database 120. Material parameters mayinclude graphical properties, such as reflectivity or glossiness, aswell as physical properties, such as softness or flexibility. Thematerial parameters may be defined based on a set of interactionvariables, wherein a texel assigned a parameter may be interacted withby a user or by AR content based on the associated material parameter.

For example, the material parameter may include a reflectivity value,such that a high reflectivity value may correspond with a mirrored, highreflective surface (i.e., a mirror), while a low reflectivity valuewould correspond with a non-reflective surface (i.e., a brick wall).

At operation 504, subsequent to assigning the material parameters to theclass, the image module 302 detects a texel of a semantic texture mapthat includes a semantic feature label that corresponds with the class.For example, a user of the client device 102 may display a presentationof a location that includes an object. Responsive to detecting theclient device 102 at the location, or detecting the object within thepresentation of the location, the semantic texture map system 124accesses a semantic texture map that corresponds with the location orobject, and applies the semantic texture map to one or more relevantsurfaces. For example, the semantic texture map may be overlaid upon theobject by positioning a 3D mesh model associated with the object at aposition based on the position of the object within the presentation ofthe environment, and then wrapping the 3D mesh model with the texels ofthe UV map that includes a set of semantic feature labels that includethe feature label associated with the class.

At operation 506, AR content may be presented in the presentation of thespace based on the material parameter of the texel of the semantictexture map. As an illustrative example, the material parameter mayinclude a high reflectivity value, and the AR content may include adisplay of an animated graphical avatar at a position within thepresentation of the space. Upon detecting the material parameter of thetexel, the AR content may be presented accordingly, such that areflection of the animated graphical avatar may appear in the texel.

FIG. 6 is a flowchart illustrating a method 600 for applying a semantictexture map to a presentation of an environment at a client device 102,according to certain example embodiments. Operations of the method 600may be performed by the modules described above with respect to FIG. 3.As shown in FIG. 6, the method 600 includes one or more operations 602,604, and 606, that may be performed as a part of (e.g., a subroutine)the method 400 depicted in FIG. 4. For example, the method 600 may beperformed subsequent to generated a semantic texture map based on imagedata.

At operation 602, the semantic texture map system 124 receives an inputassigning the semantic texture map to a location or target object,within a database 120. For example, the semantic texture map may beassociated with a set of geolocation coordinates that identify alocation, or in further embodiments may be assigned to a set of imagefeatures that correspond with a target object.

At operation 604, the semantic texture map system 124 detects a clientdevice 102 at the location identified by the geolocation coordinatesassociated with the semantic texture map in the database 120. Forexample, a geo-fence may be configured based on the geolocationcoordinates associated with the semantic texture map, where thegeo-fence encompasses the location identified by the geolocationcoordinates.

At operation 606, responsive to detecting the client device 102 at thelocation identified by the geolocation coordinates, the semantic texturemap system 124 applies the semantic texture map associated with thelocation to a presentation of an image at the client device 102.

FIG. 7 is a flow diagram 700 illustrating a method for generating asemantic texture map, according to certain example embodiments.

The semantic texture map system 124 generates a semantic texture mapbased on one or more images that comprise image features. At operation705 of the flow diagram 700, the semantic texture map system 124accesses a repository, such as the database 120, wherein the repositorycomprises a collection of images depicting a target object or locationof interest.

As seen in the flow diagram 700, the collection of images may forexample include image depicting a target object, such as the house 725.Responsive to accessing the collection of images, at operation 710 thevarious modules of the semantic texture map system 124 generates a 3Dmesh model 730 of a target object depicted in the images, by applyingthe collection of images to a 3D reconstruction pipeline. The 3D meshmodel 730 comprises a set of vertices that include coordinatesindicating positions of the vertices relative to one another.

At operation 715, as discussed in operation 408 of the method 400, thesemantic texture map system 124 generates a UV map 735 based on thecoordinates of the plurality of vertices. The UV map 735 comprises a setof 2D texture coordinates based on the coordinates of the plurality ofvertices of the 3D mesh model. As an illustrative example, the UV map735 may be described as an “unwrapped” layer of the 3D mesh model 730,that defines a set of 2D coordinates which can be used to apply graphicsto the 3D mesh model 730.

At operation 720, the semantic texture map system 124 generates asemantic segmentation image 740 based on the collection of imagesincluding the image 725, by applying the collection of images to asemantic segmentation neural network. The semantic segmentation image740 generated by the semantic texture map system 124 provides semanticfeature labels to one or more objects depicted in the collection ofimages. According to certain embodiments, the semantic segmentationimage 740 may include semantic feature labels that describe one or moreproperties of a texel that include texture properties, physicalproperties, and graphical properties.

As discussed in operation 410 of the method 400, the semanticsegmentation image 740 is projected upon the 3D mesh model 725 based onthe coordinates from the map 735, and the semantic feature labels of thesemantic segmentation image 740 are mapped to the coordinates of the UVmap 735 in order to generate a semantic texture map.

FIG. 8 is an interface diagram 800 depicting AR content 805, 810, and815 presented based on semantic labels, such as the semantic labels fromthe semantic segmentation image 740 of FIG. 7, according to certainexample embodiments.

As seen in the interface diagram 800, AR content 805, 810, and 815 maybe presented based on the semantic feature labels of the semantictexture map generated by the semantic texture map system 124. Forexample, as illustrated in FIG. 8, the AR content 805, 810, and 815 mayinclude graphical elements presented at specific positions in texelsdisplayed an image based on the semantic feature labels of the semantictexture map. For example, the texel 820 (emphasized by bolded lines),may include a set of semantic feature labels 825, where the semanticfeature labels identify properties of the texel. As seen in theinterface diagram 800, the semantic feature labels 825 of the texel 820may indicate that the texel includes a window. The semantic feature mapsystem 124 may therefore generate and present the AR content 815 basedon one of more of the semantic feature labels 825, providing an addedlayer of realism to the AR content.

Software Architecture

FIG. 9 is a block diagram illustrating an example software architecture906, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 9 is a non-limiting example of asoftware architecture and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 906 may execute on hardwaresuch as the machine 1000 of FIG. 10 that includes, among other things,processors 1004, memory 1014, and I/O components 1018. A representativehardware layer 952 is illustrated and can represent, for example, themachine 900 of FIG. 9. The representative hardware layer 952 includes aprocessing unit 954 having associated executable instructions 904.Executable instructions 904 represent the executable instructions of thesoftware architecture 906, including implementation of the methods,components and so forth described herein. The hardware layer 952 alsoincludes memory and/or storage modules memory/storage 956, which alsohave executable instructions 904. The hardware layer 952 may alsocomprise other hardware 958.

In the example architecture of FIG. 9, the software architecture 906 maybe conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 906 mayinclude layers such as an operating system 902, libraries 920,applications 916 and a presentation layer 914. Operationally, theapplications 916 and/or other components within the layers may invokeapplication programming interface (API) API calls 908 through thesoftware stack and receive a response as in response to the API calls908. The layers illustrated are representative in nature and not allsoftware architectures have all layers. For example, some mobile orspecial purpose operating systems may not provide aframeworks/middleware 918, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 902 may manage hardware resources and providecommon services. The operating system 902 may include, for example, akernel 922, services 924 and drivers 926. The kernel 922 may act as anabstraction layer between the hardware and the other software layers.For example, the kernel 922 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 924 may provideother common services for the other software layers. The drivers 926 areresponsible for controlling or interfacing with the underlying hardware.For instance, the drivers 926 include display drivers, camera drivers,Bluetooth® drivers, flash memory drivers, serial communication drivers(e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audiodrivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 920 provide a common infrastructure that is used by theapplications 916 and/or other components and/or layers. The libraries920 provide functionality that allows other software components toperform tasks in an easier fashion than to interface directly with theunderlying operating system 902 functionality (e.g., kernel 922,services 924 and/or drivers 926). The libraries 920 may include systemlibraries 944 (e.g., C standard library) that may provide functions suchas memory allocation functions, string manipulation functions,mathematical functions, and the like. In addition, the libraries 920 mayinclude API libraries 946 such as media libraries (e.g., libraries tosupport presentation and manipulation of various media format such asMPREG4, 11.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., anOpenGL framework that may be used to render 2D and 3D in a graphiccontent on a display), database libraries (e.g., SQLite that may providevarious relational database functions), web libraries (e.g., WebKit thatmay provide web browsing functionality), and the like. The libraries 920may also include a wide variety of other libraries 948 to provide manyother APIs to the applications 916 and other softwarecomponents/modules.

The frameworks/middleware 918 (also sometimes referred to as middleware)provide a higher-level common infrastructure that may be used by theapplications 916 and/or other software components/modules. For example,the frameworks/middleware 918 may provide various graphic user interface(GUI) functions, high-level resource management, high-level locationservices, and so forth. The frameworks/middleware 918 may provide abroad spectrum of other APIs that may be utilized by the applications916 and/or other software components/modules, some of which may bespecific to a particular operating system 902 or platform.

The applications 916 include built-in applications 938 and/orthird-party applications 940. Examples of representative built-inapplications 938 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. Third-party applications 940 may include anapplication developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 940 may invoke the API calls 908 provided bythe mobile operating system (such as operating system 902) to facilitatefunctionality described herein.

The applications 916 may use built in operating system functions (e.g.,kernel 922, services 924 and/or drivers 926), libraries 920, andframeworks/middleware 918 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systemsinteractions with a user may occur through a presentation layer, such aspresentation layer 914. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 10 is a block diagram illustrating components of a machine 1000,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 10 shows a diagrammatic representation of the machine1000 in the example form of a computer system, within which instructions1010 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1000 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1010 may be used to implement modules or componentsdescribed herein. The instructions 1010 transform the general,non-programmed machine 1000 into a particular machine 1000 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1000 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1000 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1000 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1010, sequentially or otherwise, that specify actions to betaken by machine 1000. Further, while only a single machine 1000 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1010 to perform any one or more of the methodologiesdiscussed herein.

The machine 1000 may include processors 1004, memory memory/storage1006, and I/O components 1018, which may be configured to communicatewith each other such as via a bus 1002. The memory/storage 1006 mayinclude a memory 1014, such as a main memory, or other memory storage,and a storage unit 1016, both accessible to the processors 1004 such asvia the bus 1002. The storage unit 1016 and memory 1014 store theinstructions 1010 embodying any one or more of the methodologies orfunctions described herein. The instructions 1010 may also reside,completely or partially, within the memory 1014, within the storage unit1016, within at least one of the processors 1004 (e.g., within theprocessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 1000. Accordingly, the memory 1014, thestorage unit 1016, and the memory of processors 1004 are examples ofmachine-readable media.

The I/O components 1018 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1018 that are included in a particular machine 1000 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1018 may include many other components that are not shown inFIG. 10, The I/O components 1018 are grouped according to functionalitymerely for simplifying the following discussion and the grouping is inno way limiting. In various example embodiments, the I/O components 1018may include output components 1026 and input components 1028. The outputcomponents 1026 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1028 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1018 may includebiometric components 1030, motion components 1034, environmentalenvironment components 1036, or position components 1038 among a widearray of other components. For example, the biometric components 1030may include components to detect expressions (e.g., hand expressions,facial expressions, vocal expressions, body gestures, or eye tracking),measure biosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1034 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 1036 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometer that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 1038 mayinclude location sensor components (e.g., a Global Position system (GPS)receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1018 may include communication components 1040operable to couple the machine 1000 to a network 1032 or devices 1020via coupling 1022 and coupling 1024 respectively. For example, thecommunication components 1040 may include a network interface componentor other suitable device to interface with the network 1032. In furtherexamples, communication components 1040 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NEC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1020 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a UniversalSerial Bus (USB)).

Moreover, the communication components 1040 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1040 may include Radio Frequency Identification(RFID) tag reader components, NEC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1040, such as, location via Internet Protocol (IP) geo-location,location via Wi-Fi® signal triangulation, location via detecting a NECbeacon signal that may indicate a particular location, and so forth.

Glossary

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine, and includes digital or analog communications signals orother intangible medium to facilitate communication of suchinstructions. Instructions may be transmitted or received over thenetwork using a transmission medium via a network interface device andusing any one of a number of well-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine that interfaces toa communications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smart phones, tablets, ultra books, netbooks,laptops, multi-processor systems, microprocessor-based or programmableconsumer electronics, game consoles, set-top boxes, or any othercommunication device that a user may use to access a network.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network that may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network may include a wireless or cellular network andthe coupling may be a Code Division Multiple Access (CDMA) connection, aGlobal System for Mobile communications (GSM) connection, or other typeof cellular or wireless coupling. In this example, the coupling mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (CPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High Speed Packet Access (IISPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard setting organizations,other long range protocols, or other data transfer technology.

“EMPHEMERAL MESSAGE” in this context refers to a message that isaccessible for a time-limited duration. An ephemeral message may be atext, an image, a video and the like. The access time for the ephemeralmessage may be set by the message sender. Alternatively, the access timemay be a default setting or a setting specified by the recipient.Regardless of the setting technique, the message is transitory.

“MACHINE-READABLE MEDIUM” in this context refers to a component, deviceor other tangible media able to store instructions and data temporarilyor permanently and may include, but is not be limited to, random-accessmemory (RAM), read-only memory (ROM), buffer memory, flash memory,optical media, magnetic media, cache memory, other types of storage(e.g., Erasable Programmable Read-Only Memory (EEPROM)) and/or anysuitable combination thereof. The term “machine-readable medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions. The term “machine-readable medium” shallalso be taken to include any medium, or combination of multiple media,that is capable of storing instructions (e.g., code) for execution by amachine, such that the instructions, when executed by one or moreprocessors of the machine, cause the machine to perform any one or moreof the methodologies described herein. Accordingly, a “machine-readablemedium” refers to a single storage apparatus or device, as well as“cloud-based” storage systems or storage networks that include multiplestorage apparatus or devices. The term “machine-readable medium”excludes signals per se.

“COMPONENT” in this context refers to a device, physical entity or logichaving boundaries defined by function or subroutine calls, branchpoints, application program interfaces (APIs), or other technologiesthat provide for the partitioning or modularization of particularprocessing or control functions. Components may be combined via theirinterfaces with other components to carry out a machine process. Acomponent may be a packaged functional hardware unit designed for usewith other components and a part of a program that usually performs aparticular function of related functions. Components may constituteeither software components (e.g., code embodied on a machine-readablemedium) or hardware components. A “hardware component” is a tangibleunit capable of performing certain operations and may be configured orarranged in a certain physical manner. In various example embodiments,one or more computer systems (e.g., a standalone computer system, aclient computer system, or a server computer system) or one or morehardware components of a computer system (e.g., a processor or a groupof processors) may be configured by software (e.g., an application orapplication portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a Field-Programmable Gate Array (FPGA) or an ApplicationSpecific Integrated Circuit (ASIC), A hardware component may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering embodiments in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times, Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses) between or among two or more of thehardware components. In embodiments in which multiple hardwarecomponents are configured or instantiated at different times,communications between such hardware components may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware components have access. Forexample, one hardware component may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware component may then, at alater time, access the memory device to retrieve and process the storedoutput. Hardware components may also initiate communications with inputor output devices, and can operate on a resource (e.g., a collection ofinformation). The various operations of example methods described hereinmay be performed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at leak some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)). The performance of certain of the operations may bedistributed among the processors, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the processors or processor-implemented components may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor)that manipulates data values according to control signals (e.g.,“commands”, “op codes”, “machine code”, etc.) and Which producescorresponding output signals that are applied to operate a machine. Aprocessor may, for example, be a Central Processing Unit (CPU), aReduced Instruction Set Computing (RISC) processor, a ComplexInstruction Set Computing (CISC) processor, a Graphics Processing Unit(GPU), a Digital Signal Processor (DSP), an Application SpecificIntegrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC)or any combination thereof. A processor may further be a multi-coreprocessor having two or more independent processors (sometimes referredto as “cores”) that may execute instructions contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

What is claimed is:
 1. A method comprising: detecting a client device ata location identified by geolocation coordinates; accessing a texturemap that corresponds with the location identified by the geolocationcoordinates, the texture map comprising a set of texels that include atleast a first texel that includes a semantic label; identifying amaterial parameter of the location based on at least the semantic labelof the first texel; retrieving media content from a repository based onthe material parameter that corresponds with the semantic label; causingdisplay of a presentation of a three-dimensional (3D) model associatedwith the location at the client device, the 3D model including a displayof the first texel upon a surface of the 3D model; and presenting themedia content upon the display of the first texel at the client device.2. The method of claim 1, wherein the detecting the client device at thelocation identified by the geolocation coordinates includes: detectingthe client device within a geofence that encompasses the locationidentified by the geolocation coordinates.
 3. The method of claim 1,wherein the semantic label corresponds with a material parameter, andwherein the presenting the media content within the presentation of thetexel is based on the material parameter.
 4. The method of claim 3,wherein the material parameter includes one or more of: a roughnessvalue; a metallic value; a specular value; and a base color value. 5.The method of claim 1, wherein the client device is associated with auser profile that comprises user profile data, and the retrieving themedia content includes: retrieving the media content based on the userprofile data and the semantic label.
 6. The method of claim 1, whereinthe retrieving the media content includes: retrieving the media contentbased on temporal data and the semantic label.
 7. The method of claim 1,wherein the detecting the client device at the location identified bythe geolocation coordinates includes: receiving image data from theclient device, the image data comprising a depiction of an object;identifying the object depicted by the image data, the objectcorresponding with the geolocation coordinates; and accessing thetexture map based on the geolocation coordinates.
 8. A systemcomprising: a memory; and at least one hardware processor coupled to thememory and comprising instructions that causes the system to performoperations comprising: detecting a client device at a locationidentified by geolocation coordinates; accessing a texture map thatcorresponds with the location identified by the geolocation coordinates,the texture map comprising a set of texels that include at least a firsttexel that includes a semantic label; identifying a material parameterof the location based on at least the semantic label of the first texel;retrieving media content from a repository based on the materialparameter that corresponds with the semantic label; causing display of apresentation of a three-dimensional (3D) model associated with thelocation at the client device, the 3D model including a display of thefirst texel upon a surface of the 3D model; and presenting the mediacontent upon the display of the first texel at the client device.
 9. Thesystem of claim 8, wherein the detecting the client device at thelocation identified by the geolocation coordinates includes: detectingthe client device within a geofence that encompasses the locationidentified by the geolocation coordinates.
 10. The system of claim 8,wherein the semantic label corresponds with a material parameter, andwherein the presenting the media content within the presentation of thetexel is based on the material parameter.
 11. The system of claim 10,wherein the material parameter includes one or more of: a roughnessvalue; a metallic value; a specular value; and a base color value. 12.The system of claim 8, wherein the client device is associated with auser profile that comprises user profile data, and the retrieving themedia content includes: retrieving the media content based on the userprofile data and the semantic label.
 13. The system of claim 8, whereinthe retrieving the media content includes: retrieving the media contentbased on temporal data and the semantic label.
 14. The system of claim8, wherein the detecting the client device at the location identified bythe geolocation coordinates includes: receiving image data from theclient device, the image data comprising a depiction of an object;identifying the object depicted by the image data, the objectcorresponding with the geolocation coordinates; and accessing thetexture map based on the geolocation coordinates.
 15. A non-transitorymachine-readable storage medium comprising instructions that, whenexecuted by one or more processors of a machine, cause the machine toperform operations comprising: detecting a client device at a locationidentified by geolocation coordinates; accessing a texture map thatcorresponds with the location identified by the geolocation coordinates,the texture map comprising a set of texels that include at least a firsttexel that includes a semantic label; identifying a material parameterof the location based on at least the semantic label of the first texel;retrieving media content from a repository based on the materialparameter that corresponds with the semantic label; causing display of apresentation of a three-dimensional (3D) model associated with thelocation at the client device, the 3D model including a display of thefirst texel upon a surface of the 3D model; and presenting the mediacontent upon the display of the first texel at the client device. 16.The non-transitory machine-readable storage medium of claim 15, whereinthe detecting the client device at the location identified by thegeolocation coordinates includes: detecting the client device within ageofence that encompasses the location identified by the geolocationcoordinates.
 17. The non-transitory machine-readable storage medium ofclaim 15, wherein the semantic label corresponds with a materialparameter, and wherein the presenting the media content within thepresentation of the texel is based on the material parameter.
 18. Thenon-transitory machine-readable storage medium of claim 17, wherein thematerial parameter includes one or more of: a roughness value; ametallic value; a specular value; and a base color value.
 19. Thenon-transitory machine-readable storage medium of claim 15, wherein theclient device is associated with a user profile that comprises userprofile data, and the retrieving the media content includes: retrievingthe media content based on the user profile data and the semantic label.20. The non-transitory machine-readable storage medium of claim 15,wherein the retrieving the media content includes: retrieving the mediacontent based on temporal data and the semantic label.